@article{ref1,
title="Predicting state-level firearm suicide rates: a machine learning approach using public policy data",
journal="American journal of preventive medicine",
year="2024",
author="Goldstein, Evan V. and Wilson, Fernando A.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="INTRODUCTION: Over 40,000 people die by suicide annually in the U.S., and firearms are the most lethal suicide method. There is limited evidence on the effectiveness of many state-level policies on reducing firearm suicide. The objective of this study was to identify public policies that best predict state-level firearm suicide rates. <br><br>METHODS: Data from the Centers for Disease Control and Prevention's WONDER system and the State Firearm Law Database, a longitudinal catalog of 134 firearm safety laws, were analyzed. The analysis included 1,450 observations from 50 states spanning 1991-2019. An ElasticNet regression technique was used to analyze the relationship between the policy variables and firearm suicide rates. Nested cross-validation was performed to tune the model hyperparameters. The study data were collected and analyzed in 2023 and 2024. <br><br>RESULTS: The optimized ElasticNet approach had a mean MSE of 2.07, which was superior to non-regularized and dummy regressor models. The most influential policies for predicting the firearm suicide rate on average included laws requiring firearm dealers that sell handguns to have a state license and laws requiring individuals to obtain a permit to purchase a firearm through an approval process that includes law enforcement, among others. <br><br>CONCLUSIONS: On average, firearm suicide rates were lower in state-years that had each influential policy active. Notably, these analyses were ecological and non-causal. However, this study was able to use a supervised machine learning approach with inherent feature selection and many policy types to make predictions using unseen data (i.e., balancing Lasso and Ridge regularization penalties).<p /> <p>Language: en</p>",
language="en",
issn="0749-3797",
doi="10.1016/j.amepre.2024.06.015",
url="http://dx.doi.org/10.1016/j.amepre.2024.06.015"
}